State-of-the-art single-view 360-degree room layout reconstruction methods formulate the problem as a high-level 1D (per-column) regression task. On the other hand, traditional low-level 2D layout segmentation is simpler to learn and can represent occluded regions, but it requires complex post-processing for the targeting layout polygon and sacrifices accuracy. We present Seg2Reg to render 1D layout depth regression from the 2D segmentation map in a differentiable and occlusion-aware way, marrying the merits of both sides. Specifically, our model predicts floor-plan density for the input equirectangular 360-degree image. Formulating the 2D layout representation as a density field enables us to employ `flattened' volume rendering to form 1D layout depth regression. In addition, we propose a novel 3D warping augmentation on layout to improve generalization. Finally, we re-implement recent room layout reconstruction methods into our codebase for benchmarking and explore modern backbones and training techniques to serve as the strong baseline. Our model significantly outperforms previous arts. The code will be made available upon publication.

翻译：当前最先进的单视角360度室内布局重建方法将问题建模为高层一维（每列）回归任务。另一方面，传统的低层二维布局分割更易学习且能表达遮挡区域，但需要针对目标布局多边形进行复杂后处理并牺牲精度。我们提出Seg2Reg方法，以可微且感知遮挡的方式从二维分割图渲染一维布局深度回归，融合了双方优势。具体而言，我们的模型对输入的等距柱状投影360度图像预测楼层平面密度场。将二维布局表示为密度场使我们能够采用"展平"体素渲染技术形成一维布局深度回归。此外，我们提出新颖的布局三维形变增强方法以提升泛化能力。最后，我们在代码库中重新实现近期室内布局重建方法以建立基准，并探索现代骨干网络与训练技术作为强基线。本模型显著超越先前最优方法。代码将于发表后开源。